Performance analysis of AR-model-based linear predictor with Kalman filtering algorithm for wireless communication systems

Wataru Yamada, Motoharu Sasaski, Takatoshi Sugiyama, Oliver Holland, Hamid Aghvami

Research output: Chapter in Book/Report/Conference proceedingOther chapter contributionpeer-review

Abstract

This paper reports the performance analysis of a proposed auto-regressive (AR) model-based linear predictor algorithm with Kalman filtering (KF). The relationship between the optimum AR order and the channel correlation coefficient is investigated by means of the Akaike Information Criterion (AIC). Through our analysis, 2nd-order differential model based on the AR model-based linear predictor algorithm with KF has the best performance and prediction accuracy. Its performance is about 0.5dB better than a linear predictor algorithm.

Original languageEnglish
Title of host publicationIEEE iWEM 2014 - IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages245-246
Number of pages2
ISBN (Electronic)9781479948154
DOIs
Publication statusPublished - 20 Nov 2014
Event2014 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, IEEE iWEM 2014 - Sapporo, Japan
Duration: 4 Aug 20146 Aug 2014

Conference

Conference2014 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, IEEE iWEM 2014
Country/TerritoryJapan
CitySapporo
Period4/08/20146/08/2014

Keywords

  • AR model
  • Channel correlation coefficient
  • Channel prediction algorithms
  • Kalman filtering

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